Comparing Predictors in Multivariate Regression Models: An Extension of Dominance Analysis

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Relative Importance Analysis: A Useful Supplement to Regression Analysis (2011, background): “More recently, Azen and Budescu (2006) and LeBreton and Tonidandel (2008) extended general dominance weight analysis and relative weight analysis, respectively, to multivariate multiple regression designs that model relationships between a set of multiple predictor variables and a set of multiple ”
Beyond Global Measures of Relative Importance: Some Insights from Dominance Analysis (2004, background): “Recently, Azen and Budescu (2004) have extended DA to the multivariate case.”
Simple means to improve the interpretability of regression coefficients (2010, methodology): “Among the several methods that have been proposed are dominance analysis (Budescu 1993; Azen & Budescu 2003, 2006), Johnson’s relative weight (Johnson 2000) and the Pratt’s product measure (see Chao et al. 2008 for a review).”
Determining the Relative Importance of Predictors in Logistic Regression: An Extension of Relative Weight Analysis (2010, methodology): “Although three forms of dominance analysis exist for comparing specific sets of predictors with one another, Azen and Budescu (2003) presented the criterion of general dominance for the ranking and scaling of importance across all the predictors.”